Prompt

How do I choose between different repo-level agents for code changes, test execution, and approval handling?

Latest observation

Jul 20, 2026 · OpenAI APIWeb search: off

A good way to choose is to map each repo-level agent to the kind of risk and feedback loop you want.

1) For code changes

Use an agent when you want it to:

  • make edits across multiple files,
  • follow existing project conventions,
  • implement a well-scoped task,
  • and produce a diff you can review.

Choose the agent that is best at:

  • local correctness: can it update code without breaking nearby logic?
  • repo awareness: does it understand project structure and patterns?
  • change size: is it better for small surgical edits or larger refactors?

Rule of thumb:

  • Small, explicit changes: use the most direct code-editing agent.
  • Broader refactors: use the agent with stronger planning and multi-file coordination.

2) For test execution

Use a test-running agent when you need:

  • verification after changes,
  • iterative debugging,
  • coverage of unit/integration tests,
  • or automated feedback on failures.

Choose based on:

  • speed: fast feedback for tight iteration loops,
  • coverage: can it run the right subset of tests,
  • diagnostic quality: does it surface useful failure output,
  • environment fit: does it know how to run tests in this repo.

Rule of thumb:

  • If you’re still editing frequently, prefer the fastest relevant test agent.
  • If you’re near completion, use the more complete test suite agent.

3) For approval handling

Use an approval-aware agent when the workflow includes:

  • review gates,
  • human sign-off,
  • security or compliance checks,
  • or controlled merge/release steps.

Choose based on:

  • policy enforcement: does it stop on required approvals?
  • handoff clarity: does it clearly show what needs review?
  • auditability: can it explain what changed and why?
  • automation level: does it only request approval, or also route/track it?

Rule of thumb:

  • If approvals are required before merge/deploy, use the agent that explicitly manages approvals and review state.
  • If approval is informal, a normal code agent plus a clear summary may be enough.

Practical selection guide

Ask yourself:

  1. Am I changing code?
    → Use a code-change agent.

  2. Do I need verification?
    → Use a test agent after or during edits.

  3. Does this need human sign-off or policy gating?
    → Use an approval-handling agent.

Common workflow

A typical sequence is:

  1. Code-change agent makes the edits.
  2. Test agent runs targeted tests.
  3. Approval-handling agent packages the result for review and gates merge/deploy.

If you’re deciding between multiple agents

Compare them on:

  • scope they can handle,
  • quality of diffs,
  • test execution reliability,
  • how they report failures,
  • and how they integrate with your approval process.

If you want, I can help you build a simple decision matrix for the specific agents in your repo.

Observation history

See 1 earlier observation

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Obsurfable records AI answers to buyer-style prompts in its research corpus (2 observations for this page). Metrics are distributions over observations, not a single static ranking.

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